import cv2
import time
import threading
import math
 
carCascade = cv2.CascadeClassifier('myhaar.xml')
video = cv2.VideoCapture('video/cars.mp4')
 
WIDTH = 1280
HEIGHT = 720
 
 
def estimateSpeed(location1, location2):
    d_pixels = math.sqrt(math.pow(location2[0] - location1[0], 2) + math.pow(location2[1] - location1[1], 2))
    # ppm = location2[2] / carWidht
    ppm = 8.8
    d_meters = d_pixels / ppm
    #print("d_pixels=" + str(d_pixels), "d_meters=" + str(d_meters))
    fps = 18
    speed = d_meters * fps * 3.6
    return speed
     
 
def trackMultipleObjects():
    rectangleColor = (0, 255, 0)
    frameCounter = 0
    currentCarID = 0
    fps = 0
     
    carTracker = {}
    carNumbers = {}
    carLocation1 = {}
    carLocation2 = {}
    speed = [None] * 1000
     
    # Write output to video file
    out = cv2.VideoWriter('outpy.avi',cv2.VideoWriter_fourcc('M','J','P','G'), 10, (WIDTH,HEIGHT))
 
 
    while True:
        start_time = time.time()
        rc, image = video.read()
        if type(image) == type(None):
            break
         
        image = cv2.resize(image, (WIDTH, HEIGHT))
        resultImage = image.copy()
         
        frameCounter = frameCounter + 1
         
        carIDtoDelete = []
 
        for carID in carTracker.keys():
            trackingQuality = carTracker[carID].update(image)
             
            if trackingQuality < 7:
                carIDtoDelete.append(carID)
                 
        for carID in carIDtoDelete:
            print ('Removing carID ' + str(carID) + ' from list of trackers.')
            print ('Removing carID ' + str(carID) + ' previous location.')
            print ('Removing carID ' + str(carID) + ' current location.')
            carTracker.pop(carID, None)
            carLocation1.pop(carID, None)
            carLocation2.pop(carID, None)
         
        if not (frameCounter % 10):
            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
            cars = carCascade.detectMultiScale(gray, 1.1, 13, 18, (24, 24))
             
            for (_x, _y, _w, _h) in cars:
                x = int(_x)
                y = int(_y)
                w = int(_w)
                h = int(_h)
             
                x_bar = x + 0.5 * w
                y_bar = y + 0.5 * h
                 
                matchCarID = None
             
                for carID in carTracker.keys():
                    trackedPosition = carTracker[carID].get_position()
                     
                    t_x = int(trackedPosition.left())
                    t_y = int(trackedPosition.top())
                    t_w = int(trackedPosition.width())
                    t_h = int(trackedPosition.height())
                     
                    t_x_bar = t_x + 0.5 * t_w
                    t_y_bar = t_y + 0.5 * t_h
                 
                    if ((t_x <= x_bar <= (t_x + t_w)) and (t_y <= y_bar <= (t_y + t_h)) and (x <= t_x_bar <= (x + w)) and (y <= t_y_bar <= (y + h))):
                        matchCarID = carID
                 
                if matchCarID is None:
                    print ('Creating new tracker ' + str(currentCarID))
                     
                    tracker = dlib.correlation_tracker()
                    tracker.start_track(image, dlib.rectangle(x, y, x + w, y + h))
                     
                    carTracker[currentCarID] = tracker
                    carLocation1[currentCarID] = [x, y, w, h]
 
                    currentCarID = currentCarID + 1
         
        #cv2.line(resultImage,(0,480),(1280,480),(255,0,0),5)
 
 
        for carID in carTracker.keys():
            trackedPosition = carTracker[carID].get_position()
                     
            t_x = int(trackedPosition.left())
            t_y = int(trackedPosition.top())
            t_w = int(trackedPosition.width())
            t_h = int(trackedPosition.height())
             
            cv2.rectangle(resultImage, (t_x, t_y), (t_x + t_w, t_y + t_h), rectangleColor, 4)
             
            # speed estimation
            carLocation2[carID] = [t_x, t_y, t_w, t_h]
         
        end_time = time.time()
         
        if not (end_time == start_time):
            fps = 1.0/(end_time - start_time)
         
        #cv2.putText(resultImage, 'FPS: ' + str(int(fps)), (620, 30),cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
 
 
        for i in carLocation1.keys():   
            if frameCounter % 1 == 0:
                [x1, y1, w1, h1] = carLocation1[i]
                [x2, y2, w2, h2] = carLocation2[i]
         
                # print 'previous location: ' + str(carLocation1[i]) + ', current location: ' + str(carLocation2[i])
                carLocation1[i] = [x2, y2, w2, h2]
         
                # print 'new previous location: ' + str(carLocation1[i])
                if [x1, y1, w1, h1] != [x2, y2, w2, h2]:
                    if (speed[i] == None or speed[i] == 0) and y1 >= 275 and y1 <= 285:
                        speed[i] = estimateSpeed([x1, y1, w1, h1], [x2, y2, w2, h2])
 
                    #if y1 > 275 and y1 < 285:
                    if speed[i] != None and y1 >= 180:
                        cv2.putText(resultImage, str(int(speed[i])) + " km/hr", (int(x1 + w1/2), int(y1-5)),cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255, 255, 255), 2)
                     
                    #print ('CarID ' + str(i) + ': speed is ' + str("%.2f" % round(speed[i], 0)) + ' km/h.\n')
 
                    #else:
                    #   cv2.putText(resultImage, "Far Object", (int(x1 + w1/2), int(y1)),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
 
                        #print ('CarID ' + str(i) + ' Location1: ' + str(carLocation1[i]) + ' Location2: ' + str(carLocation2[i]) + ' speed is ' + str("%.2f" % round(speed[i], 0)) + ' km/h.\n')
        cv2.imshow('result', resultImage)
        # Write the frame into the file 'output.avi'
        #out.write(resultImage)
 
 
        if cv2.waitKey(33) == 27:
            break
     
    cv2.destroyAllWindows()
 
if __name__ == '__main__':
    trackMultipleObjects()